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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Derry Pramono Adi
Universitas Narotama
Computer Science & IT Control & Systems Engineering Engineering

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Optimizing Virtual Resources Management Using Docker on Cloud Applications Rendra Felani; Moh Noor Al Azam; Derry Pramono Adi; Agung Widodo; Agustinus Bimo Gumelar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 3 (2020): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.57565

Abstract

This study aims to optimize servers with low utility levels on hardware using container virtualization techniques from Docker. This study's primary focus is to maximize the work of the CPU, RAM, and Hard Drive. The application of virtualization techniques is to create many containers as each of the containers is for the application to run a cloud storage system with the CaaS service infrastructure concept (Container as a Service). Containers on infrastructure will interact with other containers using configuration commands at Docker to form an infrastructure service such as CaaS in general. Testing of hardware carried out by running five Nextcloud cloud storage applications and five MariaDB database applications running in Docker containers and tested by random testing using a multimedia dataset. Random testing with datasets includes uploading and downloading datasets simultaneously and CPU monitoring under load, RAM, and Disk hardware resources. The testing will be done using Docker stats, HTOP, and Cockpit monitoring tools to determine the hardware capabilities when processing multimedia datasets.
Deteksi Emosi Wicara pada Media On-Demand menggunakan SVM dan LSTM Ainurrochman; Derry Pramono Adi; Agustinus Bimo Gumelar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (620.798 KB) | DOI: 10.29207/resti.v4i5.2073

Abstract

To date, there are many speech data sets with emotional classes, but with impromptu or intentional actors. The native speakers are given a stimulus in each emotion expression. Because natural conversation from secretly recorded daily communication still raises ethical issues, then using voice data that takes samples from movies and podcasts is the most appropriate step to take the best insights from speech. Professional actors are trained to induce the most real emotions close to natural, through the Stanislavski acting method. The speech dataset that meets this qualification is the Human voice Natural Language from On-demand media (HENLO). Within HENLO, there are basic per-emotion audio clips of films and podcasts originating from Media On-Demand, a motion video entertainment media platform with the freedom to play and download at any time. In this paper, we describe the use of sound clips from HENLO, then conduct learning using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). In these two methods, we found the best strategy by training LSTMs first, then then feeding the model to SVM, with a data split strategy at 80:20 scale. The results of the five training phases show that the last accuracy results increased by more than 17% compared to the first training. These results mean both complement and methods are important for improving classification accuracy.
Co-Authors Adi Nugroho Agung Widodo Agustinus Bimo Gumelar Ainurrochman Moh Noor Al Azam Rendra Felani